Development of a Method for Evaluating Drug‐Likeness and Ease of Synthesis Using a Data Set in which Compounds Are Assigned Scores Based on Chemists′ Intuition.

Takaoka, Yuji; Endo, Yutaka; Yamanobe, Susumu; Kakinuma, Hiroyuki; Okubo, Taketoshi; Shimazaki, Youichi; Ota, Tomomi; Sumiya, Shigeyuki; Yoshikawa, Kensei

doi:10.1002/chin.200340233

Cited by 30 publications

(41 citation statements)

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“…In order to develop more reliable prediction models of drug-likeness, a large number of molecular descriptors and numerous machine learning approaches have been employed, such as support vector machine (SVM) [40][41][42][43]67], neural networks (NN) [37,38,40,41,67,68], genetic algorithm (GA) [69][70][71], recursive partitioning (RP) [45,72], etc. Encouragingly, most prediction models for drug-likeness predictions established by machine learning approaches show satisfactory abilities to discriminate between drug-like and non-drug-like molecules.…”

Section: Prediction Models Based On Machine Learning Approachesmentioning

confidence: 99%

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The application of in silico drug-likeness predictions in pharmaceutical research

Tian

Wang

et al. 2015

Advanced Drug Delivery Reviews

360

205

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Section: Prediction Models Based On Machine Learning Approachesmentioning

confidence: 99%

“…The previous studies showed that the drug-likeness models established by SVM have better prediction accuracies than those established by ANN or RP[40,41,67,72]. In 2003, Takaoka et al built several drug-likeness models based on SVM and ANN [67], and found that the SVM drug-likeness models are superior to the ANN models.…”

mentioning

confidence: 99%

The application of in silico drug-likeness predictions in pharmaceutical research

Tian

Wang

et al. 2015

Advanced Drug Delivery Reviews

360

205

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“…2 Current procedures for quantifying synthesizability are based on (1) structure complexity and similarity or (2) synthetic pathways. The structure-based approach usually involves constructing a heuristic definition based on domain expertise or chemical substructure diversity 14,15 or designing a model that can be fit to expert scores [16][17][18] or reaction data. 19,20 This kind of method is widely used due to its ease of implementation and low computational cost.…”

Section: Introductionmentioning

confidence: 99%

The Synthesizability of Molecules Proposed by Generative Models

Gao

Coley

2020

J. Chem. Inf. Model.

248

274

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The discovery of functional molecules is an expensive and time-consuming process, exemplified by the rising costs of small molecule therapeutic discovery. One class of techniques of growing interest for early-stage drug discovery is de novo molecular generation and optimization, catalyzed by the development of new deep learning approaches. 1 These techniques can suggest novel molecular structures intended to maximize a multi-objective function, e.g., suitability as a therapeutic against a particular target, 2 without relying on brute-force exploration of a chemical space. 3 However, the utility of these approaches is stymied by ignorance of synthesizability. To highlight the severity of this issue, we use a data-driven computer-aided synthesis planning program 4 to quantify how often molecules proposed by state-of-the-art generative models cannot be readily synthesized. Our analysis demonstrates that there are several tasks for which these models generate unrealistic molecular structures despite performing well on popular quantitative benchmarks. Synthetic complexity heuristics can successfully bias generation toward synthetically-tractable chemical space, although doing so 1 arXiv:2002.07007v1 [q-bio.QM] 17 Feb 2020 necessarily detracts from the primary objective. This analysis suggests that to improve the utility of these models in real discovery workflows, new algorithm development is warranted.

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“…Computational tools have been developed over the past four decades to help the synthetic chemists (and/or their CADD colleagues) find a viable synthetic route for a novel molecule. They can be broadly categorized into two classes: synthesizability estimation [5][6][7][8][9][10][11][12][13] ; and synthetic route prediction (variously called computer assisted synthesis design (CASD), computer-assisted organic synthesis (CAOS), computer-assisted synthesis planning (CASP), or computer-assisted reaction design (CARD)) [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32] . These tools had their heyday during the 1980s and 1990s but subsequently fell out of favor as an approach used in practice, and the entire field went essentially dormant for a good decade until the field experienced a revival of sorts in the 2010s.…”

Section: Background and Summarymentioning

confidence: 99%

SAVI, in silico generation of billions of easily synthesizable compounds through expert-system type rules

Patel

Ihlenfeldt²,

Judson³

et al. 2020

Sci Data

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We have made available a database of over 1 billion compounds predicted to be easily synthesizable, called Synthetically Accessible Virtual Inventory (SAVI). They have been created by a set of transforms based on an adaptation and extension of the CHMTRN/PATRAN programming languages describing chemical synthesis expert knowledge, which originally stem from the LHASA project. The chemoinformatics toolkit CACTVS was used to apply a total of 53 transforms to about 150,000 readily available building blocks (enamine.net). Only single-step, two-reactant syntheses were calculated for this database even though the technology can execute multi-step reactions. The possibility to incorporate scoring systems in CHMTRN allowed us to subdivide the database of 1.75 billion compounds in sets according to their predicted synthesizability, with the most-synthesizable class comprising 1.09 billion synthetic products. Properties calculated for all SAVI products show that the database should be well-suited for drug discovery. It is being made publicly available for free download from https://doi.org/10.35115/37n9-5738.

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Development of a Method for Evaluating Drug‐Likeness and Ease of Synthesis Using a Data Set in which Compounds Are Assigned Scores Based on Chemists′ Intuition.

Abstract: Intuition. -(TAKAOKA*, Y.; ENDO, Y.; YAMANOBE, S.; KAKINUMA, H.; OKUBO, T.; SHIMAZAKI, Y.; OTA, T.; SUMIYA, S.; YOSHIKAWA, K.; J. Chem.

Cited by 30 publications

References 1 publication

The application of in silico drug-likeness predictions in pharmaceutical research

The application of in silico drug-likeness predictions in pharmaceutical research

The Synthesizability of Molecules Proposed by Generative Models

SAVI, in silico generation of billions of easily synthesizable compounds through expert-system type rules

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